COMPUTER-ASSISTED ANALYSIS OF MEDULLOBLASTOMA - A CYTOLOGIC STUDY

OBJECTIVE: To explore data from a set of cases of medulloblastoma to s ee whether quantitative image analysis might suggest evidence for the existence of lower and higher grade lesions. STUDY DESIGN: Fourteen co nsecutive cases of medulloblastoma were obtained. Smears were stained with toluidine blue. For each case, 50 nuclei were measured and a numb er of densitometric features extracted. RESULTS: The existence of two subgroups of cases, identified as lower and higher grade groups, was s uggested by a plot of the total optical density versus nuclear area. T wo nuclear texture features-the number of pixels with the same optical density value occurring consecutively in the nucleus and the proporti on of pixels in the high optical density range-divided the cases into the same subgroups. The use of a clustering algorithm established two clusters that corresponded to that subgrouping except for one case. Di scriminant analysis gave an identical classification, with the misplac ed case having a borderline discriminant function score. An unsupervis ed learning algorithm based on an adaptive distance metric formed two clusters and assigned the borderline case to the low grade subgroup. T he grouping obtained by quantitative analysis was only partly related to the grade of nuclear atypia subjectively evaluated. CONCLUSION: In our series of medulloblastomas, quantitative analysis provided a means of detecting differences in the nuclear size and texture that allowed the classification of cases into two subgroups.